Google Analytics Summit 2014 – The future of web analytics

Mario Colombo

At the Google Analytics (GA) Summit 2014, which took place last week in San Francisco, Google announced many product upgrades. And some of them are likely to take GA into a world beyond pure web analytics. Here’s why I think this matters a lot.

The Google Analytics team leaders at the GA Summit 2014 (Photo: Mario Colombo)

You can learn a lot about your customer’s behavior online with digital analytics. However, if you look really close you will realize that today’s web analytics tools face two challenges: a lack of context and sometimes suboptimal data quality. Google’s announcements at the GA Summit point to a future where these challenges are far less difficult to overcome.

The good news

Google's answer to both data quality and context challenges in web analytics is the integration with client-side systems. The objective: matching customer and product data with web analytics data. How can this be achieved? On the one hand, more and more touchpoints are online and thus become digitally measurable. On the other hand, GA is reaching out into the offline world. Some examples:

Cost Data: GA allows you to merge revenue information from a custom marketing channel with its cost data (which you might receive as an excel file from your agency). GA is then able to calculate the Return on Investment (ROI) for every marketing channel.

Rich Dimensions: Enrich your data with additional information like product subcategories or page categories within Google Analytics by importing corresponding data. GA will even let you apply your own taxonomy to historic data. (In the GA language, this is called Dimension Widening)

Offline Data: The convergence of on- and offline business activity and customer touchpoints is real. With GA’s measurement protocol it is possible to push data from a checkout in your brick-and-mortar store into GA and to combine it with that customer's website visits and online transactions.

The current state of Google Analytics

Why does this really matter? To understand you might want to look closer at the above stated challenges: quality of measurement and context.

Quality of measurement: For the time being, all major web analytics tools are based on cookie technology, which is notoriously and increasingly unreliable: users delete their cookies and change browsers as well as devices. At the same time, website technology and web analytics tracking setups become more and more complex and error-prone. It is very common that a benchmark between different web analytics tools or between a web analytics tool and a data warehouse results in significantly different numbers. Also, data sampling that was introduced to generate reports quickly has become a quality issue lately.

Context: The big challenge in web analytics is to deliver actionable insights based on the generated data. This can only be achieved by joining quantitative signals (like number of visits) with qualitative signals (unique, identifiable users with a transparent history of interaction with your business). Web analytics is struggling to provide enough contextual information that enables companies to act on its data. On the one hand, contextual information is limited due to the underlying technology, on the other hand there are significant privacy restrictions that need to be complied with.

Today, many web analysts may find it difficult to connect different data sets to gain valuable insights while looking at their data. Here are some tough questions a web analyst might run into:

Display campaign X brought a lot of traffic to the site. How much did we invest into X?

My telco’s total revenue has been increasing; its online contracts go through a credit check. How has the share of negative credit checks been trending?

On my auction platform I know how many bids have been made on a given auction. Now I’d like to find out which bids carry the most value for the platform?

On my eCommerce shop I know how many transactions have been made. I want to know now which transactions are made by new and which by existing customers.

I’d like to find out which online measures positively influences offline sales (or vice versa) of the multi-channel retailer I work for.

My B2B manufacturer can measure visitors on his website. Now he’d like to find out which of them are high potentials and what their intent is.

With GA’s new features these questions can be tackled, as GA will become more and more of a data warehouse, integrating a variety of business intelligence streams way beyond website visits.

So what?

Using these possibilities, important business questions (including those given above as examples) can be answered more accurately. At the same time, knitting a tighter net around online user interactions improves data quality significantly.

I am convinced that going forward the correct application and smart use of Google Analytics’ powerful new features will provide a sound base for strategic decisions, resulting in significant competitive advantages.